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International Conference on Communication and Electronics Systems (ICCES)

作者： P. Balaji I.K. Mohitha K. Prashanthini K. Sivaprakash P. Rithick S. Kasthuri Department of Electronics and Instrumentation Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore India Department of Robotics and Automation Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Machine learning (ML) and Internet of Things (IoT) technologies have revolutionized the healthcare industry, particularly in the field of medication administration. Inadequate adherence to medication is a serious worldwide issue that can result in poor health and higher medical expenses. Through continuous monitoring and analysis of patient-specific parameters, such as vital signs, medication history, and lifestyle factors, the system dynamically adjusts medication schedules and dosages to optimize therapeutic efficacy while minimizing adverse effects. The system tracks medicine usage, vital signs, and ambient variables using data gathered from Internet of Things (IoT) devices like wearable devices and smart pill bottles. An ML model, trained to anticipate potential adherence problems, is then provided with this preprocessed data. Personalized interventions, like medicine reminders, educational content, and progress reports, are delivered by the Intelligent Medication Compliance System (IMAC) based on these predictions. The integration of IoT-enabled smart sensors and connectivity allows for smooth communication between patients, healthcare professionals, and medicine dispensers, permitting prompt interventions, prescription reminders, and remote monitoring. Additionally, the system makes it easier for patients and medical professionals to communicate, allowing for prompt modifications to treatment programs. Serving as an anticipatory system, the IMAC provides personalized interventions. Predicting possible adherence problems, such as missing doses or inadvertent drug interactions, is done by the model through data analysis. The proposed IMAS seeks to empower patients, personalized medication management, and eventually improve medication adherence by fusing real-time IoT data with ML-driven insights.

关键词： Industries Schedules Accuracy Medical services Machine learning Predictive models Data models Internet of Things Wearable devices Smart phones

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Opportunistic Communication in Robot Teams

Opportunistic Communication in Robot Teams

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Daniel Mox Kashish Garg Alejandro Ribeiro Vijay Kumar General Robotics Automation Sensing and Perception (GRASP) Laboratory University of Pennsylvania Philadelphia PA USA Electrical and Systems Engineering Department University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper we present a new approach to Mobile Infrastructure on Demand (MID) where a dedicated team of robots creates and sustains a wireless network that satisfies the communication requirements of a different team of task-oriented robots seeking to coordinate their actions in the absence of existing communication infrastructure. Different from previous works, our approach forgoes heuristics for network performance such as algebraic-connectivity or network flow optimizations and instead positions communication support robots to directly maximize the probability of packet delivery by the underlying opportunistic routing protocol. Our system is task agnostic and practical to implement and operate on robots equipped with off-the-shelf WiFi radios. We demonstrate this through a set of experiments showing our MID system maintaining the delivery of critical mission data in a situational awareness setting and enabling foraging robots to effectively coordinate their actions during multi-robot exploration.

关键词： Robot kinematics Wireless networks Routing protocols Mobile robots Communication networks Task analysis Robots

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Enhanced RF Characteristics of Slotted Frequency-Reconfigurable Microstrip Patch Antenna Using RF MEMS Switches at 3.5 GHz

Enhanced RF Characteristics of Slotted Frequency-Reconfigura...

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Artificial Intelligence and Signal Processing (AISP), INDIA, International Conference on

作者： Narayanan R Murali G Ramkumar G Anitha Department of Automation and Robotics Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

In this study,a frequency-reconfigurable microstrip patch antenna that has a central slot and RF MEMS shunt switches is optimized and improved. The substrate material for the investigation was chosen to be FR4 epoxy, with dimensions of 40 mm by 65 mm and 1.6 mm for height. The measurements of the microstrip patch were found to be 19.5 mm by 31 mm, and the feed dimension was 16.4 mm by 1.3 mm. After preliminary research, a center slot was added and slots were added on both sides of the patch to accommodate RF MEMS switches. In order to maximize the antenna's performance, parametric analysis was carried out, which involved making iterative changes to the patch dimensions and slot characteristics. Notably, the improved antenna configuration produced a gain of 5.1 dBi and a VSWR of 1, making it appropriate for 3.5 GHz applications such as wireless local area networks (WLANs), mobile phones, and radar systems. The outcomes show how well the suggested design strategy works to improve antenna performance for real-world implementation in modern communication systems.

关键词： Radio frequency Micromechanical devices Slot antennas Microswitches Patch antennas Microstrip antennas Radar antennas Microstrip Optimization Substrates

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Predictive Models for the Early Diagnosis and Prognosis of Knee Osteoarthritis Using Deep Learning Techniques 1st

Predictive Models for the Early Diagnosis and Prognosis of K...

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1st International Conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Malathi, S.Y. Bharamagoudar, Geeta Shiragudikar, Sharada K. G. Totad, Shashikumar CSE Department KLEIT Karnataka Hubballi India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India Department of Automation and Robotics KLE Technological University Karnataka Hubballi India

ISBN: (纸本)9783031756047

Uncovering hidden patterns and relationships in knee OA data using machine learning algorithms can be incredibly useful for predicting illness progression and developing more effective treatments. By analyzing large amounts of data, machine learning algorithms can identify patterns and relationships that might not be immediately apparent to human researchers. This can help us better understand the mechanisms behind knee OA and identify potential targets for treatment. By developing more potent treatments, we may be able to slow the progression of the disease and improve patient outcomes. The knee osteoarthritis dataset information was initially acquired from Kaggle and the information on knee osteoarthritis is processed before model training. The study aims to develop and apply advanced machine learning methods, such as deep learning techniques like CNN-SVM, Mobilenet v2, Resnet152v2, Resnet 50, and EfficientnetB5, to analyze knee OA data and develop accurate prediction models for early diagnosis and prognosis. The study evaluates the performance of these models on a dataset of individuals with knee OA to determine their efficacy and accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： CNN EfficientNetB5 Knee osteoarthritis mobilenetV2 ResNet50

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CNN-based Methods for Object Recognition with High-Resolution Tactile Sensors

arXiv

引用

arXiv 2023年

作者： Gandarias, Juan M. García-Cerezo, Alfonso J. Gómez-De-Gabriel, Jesús M. Robotics and Mechatronics Group Systems Engineering and Automation Department University of Málaga Escuela de Ingenierías Industriales Málaga Spain

Novel high-resolution pressure-sensor arrays allow treating pressure readings as standard images. Computer vision algorithms and methods such as Convolutional Neural Networks (CNN) can be used to identify contact objects. In this paper, a high-resolution tactile sensor has been attached to a robotic end-effector to identify contacted objects. Two CNN-based approaches have been employed to classify pressure images. These methods include a transfer learning approach using a pre-trained CNN on an RGB-images dataset and a custom-made CNN (TactNet) trained from scratch with tactile information. The transfer learning approach can be carried out by retraining the classification layers of the network or replacing these layers with an SVM. Overall, 11 configurations based on these methods have been tested: 8 transfer learning-based, and 3 TactNet-based. Moreover, a study of the performance of the methods and a comparative discussion with the current state-of-the-art on tactile object recognition is presented. Copyright © 2023, The Authors. All rights reserved.

关键词： Object recognition

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Design and Validation of a Robotic-Assisted Transperineal Prostate Biopsy System

Design and Validation of a Robotic-Assisted Transperineal Pr...

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IEEE International Conference on robotics and Biomimetics

作者： Yaqing Wang Shaoming Li Fangxun Zhong Yunhui Liu Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China Hong Kong Centre for Logistics Robotics (HKCLR) Hong Kong China Department of Mechanical and Automation Engineering (MAE) The Chinese University of Hong Kong (CUHK) HKSAR China School of Science and Engineering CUHK-Shenzhen Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Prostate cancer is a common condition among elderly men, significantly impacting quality of life. It accounts for a large percentage of cancer cases, necessitating effective diagnostic methods. Needle biopsy is a vital procedure for accurate detection and grading of prostate cancer, allowing for targeted treatment. The most common approach is transper-ineal prostate biopsy, guided by real-time ultrasound and MRI fusion. The traditional free-hand biopsy technique is highly dependent on the surgeon's expertise and has limitations in both effectiveness and precision. Robotic-assisted biopsy aims to address these issues by enhancing the accuracy and efficiency of the procedure, reducing the need for extensive surgeon experience. This paper introduces an active six-degree-of-freedom (DOF) and one Z-direction passive DOF transperineal prostate biopsy robot incorporating a 2-DOF ultrasonic probe unit and a biopsy unit with 4-DOF movement to replace the manual operation. This system ensures precise orientation and positioning of the biopsy needle, minimizes patient risk, and supports both 2D and 3D ultrasound imaging, thus facilitating a safe and effective biopsy with improved workflow for surgeons.

关键词： Ultrasonic imaging Accuracy Three-dimensional displays Biopsy Surgery Needles Real-time systems Prostate cancer Probes Robots

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Multi-class machine learning based approach for sleep disorder identification using EEG signals

Multi-class machine learning based approach for sleep disord...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Rajalakshmi Murugesan Karthik Chandran P. K. Dutta Department of Mechatronics Thiagarajar College of Engineering Madurai Tamilnadu India Department of Robotics & Automation Jyothi Engineering College Kerala India Department of Electronics and Communication School of Engineering and Technology Amity University Kolkata India

Accurately identifying sleep stages is crucial for the analysis and management of sleep disorders. Electroencephalogram (EEG) signals have shown promise in sleep stage identification. This research presents a novel method for classifying different sleep stages using EEG data based on a multi-class support vector machine (SVM) approach. The proposed method involves preprocessing the EEG signals to remove noise and artifacts, followed by feature extraction using wavelet transform and statistical methods. A multi-class SVM classifier is then trained to identify four sleep stages: waking, non-rapid eye movement (NREM) stage 1, non-rapid eye movement (NREM) stage 2, and rapid eye movement (REM) stage. The method is evaluated on a dataset comprising EEG signals collected from 20 healthy individuals during a full-night sleep study. The results demonstrate that the proposed technique outperforms existing EEG-based approaches, achieving anaverage classification accuracy of 86.2 for sleep stage identification. The suggested method has potential applications in clinical settings for sleep disorder evaluation and management. Additionally, it can be extended to other applications requiring the identification of various classes from EEG data.

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Evolutionary-Based Online Motion Planning Framework for Quadruped Robot Jumping

arXiv

引用

arXiv 2023年

作者： Yue, Linzhu Song, Zhitao Zhang, Hongbo Zeng, Xuanqi Zhang, Lingwei Liu, Yun-Hui The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong The Hong Kong Centre for Logistics Robotics Hong Kong

Offline evolutionary-based methodologies have supplied a successful motion planning framework for the quadrupedal jump. However, the time-consuming computation caused by massive population evolution in offline evolutionary-based jumping framework significantly limits the popularity in the quadrupedal field. This paper presents a time-friendly online motion planning framework based on meta-heuristic Differential evolution (DE), Latin hypercube sampling, and Configuration space (DLC). The DLC framework establishes a multidimensional optimization problem leveraging centroidal dynamics to determine the ideal trajectory of the center of mass (CoM) and ground reaction forces (GRFs). The configuration space is introduced to the evolutionary optimization in order to condense the searching region. Latin hypercube sampling offers more uniform initial populations of DE under limited sampling points, accelerating away from a local minimum. This research also constructs a collection of pre-motion trajectories as a warm start when the objective state is in the neighborhood of the pre-motion state to drastically reduce the solving time. The proposed methodology is successfully validated via real robot experiments for online jumping trajectory optimization with different jumping motions (e.g., ordinary jumping, flipping, and spinning). © 2023, CC BY-SA.

关键词： Motion planning

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Effects of Chemical Treatment on Natural (Rice Straw/Neem Saw Dust) Fiber Morphology and Mechanical Properties of its Composite Material

SSRN

引用

SSRN 2023年

作者： Thangapandian, P. Kumar, P. Raj Kailasavalli, S. Paulsingarayar, S. Seenivasan, S. Department of Mechanical Engineering Rathinam Technical Campus India Department of Robotics and Automation Engineering Dhaanish Ahmed Institute of Technology India Department of Mathematics PSNA College of Engineering and Technology India Department of Mechanical Engineering NPR College of Engineering & Technology

Natural fibres derived from agricultural waste have gained traction in recent years. A natural fibre/rice straw hybrid composite has been created to recycle agricultural waste easily while eliminating smoke and fine silica ash pollution. As per ASTM standards, hand lay-up is used to create composite specimens. Scanning electron microscopy has studied rice straw's interfacial bonding and dispersion in unsaturated polyester resin. Composites reinforced with Rice Straw and Neem Powder (Hybrid) show better mechanical properties. The optimal fibre content is 50% by volume fraction for impact and flexural strength. Volume proportion decreases flexural strength, but volume proportion increases impact value. © 2023, The Authors. All rights reserved.

关键词： Scanning electron microscopy

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Identifying Factors that Influence Student’s Participation in an Online Discussion Forum

Identifying Factors that Influence Student’s Participation ...

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Delhi Section Conference (DELCON), IEEE

作者： Mohammed Tayyab Shaikh Unnati Koppikar Vijayalakshmi M Department of Automation and Robotics KLE Technological University Hubballi India Centre for Engineering Education Research KLE Technological University Hubli India

Discussion forums are used frequently in online learning. They are effective tools to encourage critical thinking and analytical reasoning and motivate students to participate in discussions. By actively participating in discussion forums by reading their peer’s posts and comments, students can increase their understanding by clarifying vague concepts. Due to the COVID-19 epidemic, educational institutions transitioned to an online teaching method, which was not widely used earlier. The dynamism of the lively discussions that would take place in the offline classes was lost during this time. As a result, many instructors use discussion boards to keep their students engaged in a specific topic and encourage them to debate, evaluate, and criticize one another’s ideas. The social network analysis (SNA) method examines how students engage in an online asynchronous discussion forum. The research study is based on a course called "engineering Exploration". During the COVID-19 pandemic’s disruptions, this first-year engineering course was taught online. In order to engage in a case study for the engineering Ethics module, students were invited to post their views and thoughts about the case study in the discussion thread and were informed to comment on their peer’s posts. The research is based on information gathered from the discussion board. Through this study, the authors want to identify the effect of different parameters such as gender, stream, or grades on students’ participation in the discussion forum. The course instructor will gain significant insight from the proposed work on integrating social network analysis on discussion boards. This research study will aid in understanding the network structures of learners’ interactions.

关键词： COVID-19 Ethics Epidemics Discussion forums Social networking (online) Distance learning IEEE Sections

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